[论文解读] n-Gage: Predicting in-class Emotional, Behavioural and Cognitive Engagement in the Wild

Nan Gao, Wei Shao+2 more|arXiv (Cornell University)|Jul 9, 2020

Flow Experience in Various Fields参考文献 81被引用 32

一句话总结

n-Gage 通过可穿戴设备和环境传感器在真实世界的课堂中预测高中生的多维度在课内参与感（情感、行为、认知），实现 MAE ~0.56–0.79 和 RMSE ~0.72–0.98。

ABSTRACT

The study of student engagement has attracted growing interests to address problems such as low academic performance, disaffection, and high dropout rates. Existing approaches to measuring student engagement typically rely on survey-based instruments. While effective, those approaches are time-consuming and labour-intensive. Meanwhile, both the response rate and quality of the survey are usually poor. As an alternative, in this paper, we investigate whether we can infer and predict engagement at multiple dimensions, just using sensors. We hypothesize that multidimensional student engagement can be translated into physiological responses and activity changes during the class, and also be affected by the environmental changes. Therefore, we aim to explore the following questions: Can we measure the multiple dimensions of high school student's learning engagement including emotional, behavioural and cognitive engagement with sensing data in the wild? Can we derive the activity, physiological, and environmental factors contributing to the different dimensions of student engagement? If yes, which sensors are the most useful in differentiating each dimension of the engagement? Then, we conduct an in-situ study in a high school from 23 students and 6 teachers in 144 classes over 11 courses for 4 weeks. We present the n-Gage, a student engagement sensing system using a combination of sensors from wearables and environments to automatically detect student in-class multidimensional learning engagement. Experiment results show that n-Gage can accurately predict multidimensional student engagement in real-world scenarios with an average MAE of 0.788 and RMSE of 0.975 using all the sensors. We also show a set of interesting findings of how different factors (e.g., combinations of sensors, school subjects, CO2 level) affect each dimension of the student learning engagement.

研究动机与目标

• 推动需要自动化、基于传感器的参与度测量以补充或替代耗时的调查问卷。
• 研究在野外收集的生理、活动与环境数据是否能够推断出多维度参与（情感、行为、认知）。
• 识别最能区分每个参与维度的传感器。
• 开发并验证一个将可穿戴数据与室内环境数据融合用于参与预测的课堂传感系统（n-Gage）。

提出的方法

• 从一所高中收集的 23 名学生和 6 位教师在 144 节课中为期 4 周的大型、多样化野外数据集。
• 使用 Empatica E4 手环捕捉 EDA、PPG/HRV、加速度计和皮肤温度，以及 Netatmo 室内传感器获取温度与 CO2。
• 通过 Adapted In-class Student Engagement Questionnaires (ISEQ) 记录行为、情感和认知维度的自我报告参与度。
• 通过班级时段分割（IGTS）、伪影去除、EDA 分解、HRV 估计和归一化对数据进行预处理。
• 从生理信号、活动和环境数据中提取特征，包括皮肤温度和室内环境指标。
• 使用 LightGBM 回归器对多维参与进行预测，并在所有传感器上通过 MAE 和 RMSE 进行评估。

实验结果

研究问题

• RQ1 我们是否可以使用野外传感数据测量高中生学习参与的多个维度（情感、行为、认知）？
• RQ2 哪些活动、生理和环境因素有助于区分不同的参与维度，哪些传感器最能区分它们？
• RQ3 哪些传感器和特征表示能显著提高预测每个参与维度的准确性？
• RQ4 环境因素（如 CO2）在真实课堂环境中如何影响参与维度？

主要发现

• n-Gage 在使用所有传感器时可以以 MAE 约为 0.56、RMSE 约为 0.72 的水平预测多维参与。
• 在野外数据中，使用所有传感器进行参与预测的 MAE 约为 0.788、RMSE 约为 0.975。
• 环境因素如 CO2 水平对认知参与有负向影响，凸显通风的相关性。
• 该研究提供了将生理信号、运动和室内环境数据相结合以在真实课堂中进行参与估计的实用证据。
• 在清理后，数据集包含 331 节课，涉及 23 名学生和 6 名教师，覆盖 105 节分析课程。